Quantitative Modeling of Complex Environments

Keith Decker, Victor Lesser

Intelligent Systems in Accounting Finance & Management · 1993 · 140 citations · 32 references

Concepts

TL;DR

Traditional models focus on an agent’s mental state to predict actions, but for many problems it is more realistic to center analysis on the task environment, domain, or society in which agents operate. The paper introduces a task‑environment‑oriented modeling framework that complements agent‑centered approaches. The framework models quantitative interrelationships among tasks, the timing and availability of information for updating agents’ mental states, and the overall structure of the task environment, and is illustrated through a cooperative problem‑solving model in a distributed sensor network that supports both analysis and simulation. The task‑environment model enables analysis and simulation, sidesteps the pitfalls of single‑instance examples, and yields concrete, meaningful characterizations that facilitate general theory formulation.

Abstract

Abstract There are many formal approaches to specifying how the mental state of an agent entails the particular actions it will perform. These approaches put the agent at the center of analysis. For some questions and purposes, it is more realistic and convenient for the center of analysis to be the task environment, domain or society of which agents will be a part. This paper presents such a task environment‐oriented modeling framework that can work hand in hand with more agent‐centered approaches. Our approach features careful attention to the quantitative computational interrelationships between tasks, to what information is available (and when) to update an agent's mental state and to the general structure of the task environment rather than single‐instance examples. A task environment model can be used for both analysis and simulation, it avoids the methodological problems of relying solely on single‐instance examples and provides concrete, meaningful characterizations with which to state general theories. This paper is organized around an example model of co‐operative problem solving in a distributed sensor network.

References

32